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      A Note on Estimating Optimal Dynamic Treatment Strategies Under Resource Constraints Using Dynamic Marginal Structural Models

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          Abstract

          Existing strategies for determining the optimal treatment or monitoring strategy typically assume unlimited access to resources. However, when a health system has resource constraints, such as limited funds, access to medication, or monitoring capabilities, medical decisions must balance impacts on both individual and population health outcomes. That is, decisions should account for competition between individuals in resource usage. One simple solution is to estimate the (counterfactual) resource usage under the possible interventions and choose the optimal strategy for which resource usage is within acceptable limits. We propose a method to identify the optimal dynamic intervention strategy that leads to the best expected health outcome accounting for a health system's resource constraints. We then apply this method to determine the optimal dynamic monitoring strategy for people living with HIV when resource limits on monitoring exist using observational data from the HIV-CAUSAL Collaboration.

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          Estimation and extrapolation of optimal treatment and testing strategies

          We review recent developments in the estimation of an optimal treatment strategy or regime from longitudinal data collected in an observational study. We also propose novel methods for using the data obtained from an observational database in one health-care system to determine the optimal treatment regime for biologically similar subjects in a second health-care system when, for cultural, logistical, or financial reasons, the two health-care systems differ (and will continue to differ) in the frequency of, and reasons for, both laboratory tests and physician visits. Finally, we propose a novel method for estimating the optimal timing of expensive and/or painful diagnostic or prognostic tests. Diagnostic or prognostic tests are only useful in so far as they help a physician to determine the optimal dosing strategy, by providing information on both the current health state and the prognosis of a patient because, in contrast to drug therapies, these tests have no direct causal effect on disease progression. Our new method explicitly incorporates this no direct effect restriction.
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            Optimal Individualized Treatments in Resource-Limited Settings

            An individualized treatment rule (ITR) is a treatment rule which assigns treatments to individuals based on (a subset of) their measured covariates. An optimal ITR is the ITR which maximizes the population mean outcome. Previous works in this area have assumed that treatment is an unlimited resource so that the entire population can be treated if this strategy maximizes the population mean outcome. We consider optimal ITRs in settings where the treatment resource is limited so that there is a maximum proportion of the population which can be treated. We give a general closed-form expression for an optimal stochastic ITR in this resource-limited setting, and a closed-form expression for the optimal deterministic ITR under an additional assumption. We also present an estimator of the mean outcome under the optimal stochastic ITR in a large semiparametric model that at most places restrictions on the probability of treatment assignment given covariates. We give conditions under which our estimator is efficient among all regular and asymptotically linear estimators. All of our results are supported by simulations.
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              Author and article information

              Journal
              14 February 2019
              Article
              1903.06488
              22d60cc2-c9d1-4b58-8837-207b16ed434e

              http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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              Custom metadata
              stat.AP q-bio.PE stat.ME

              Evolutionary Biology,Applications,Methodology
              Evolutionary Biology, Applications, Methodology

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